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Algolia vs Azure Search: What are the differences?
Introduction
Algolia and Azure Search are both popular search-as-a-service platforms that offer powerful search capabilities for developers. While they share some similarities, there are key differences that set them apart.
- Pricing Model: Algolia has a pricing model based on the number of operations and the amount of data indexed, while Azure Search has a pricing model based on the number of document operations and the amount of data stored. Algolia's pricing structure allows for more flexibility and transparency in terms of cost.
- Scalability: Algolia is known for its ability to handle high query volumes and provide fast search results, making it suitable for applications with a high traffic load. Azure Search, on the other hand, provides scalable search capabilities but may not offer the same level of performance as Algolia for extremely large datasets or heavy query traffic.
- Advanced Search Features: Algolia offers a rich set of advanced search features out-of-the-box, such as typo-tolerance, faceting, filtering, and geolocation search. These features make it easier for developers to enhance search experiences. While Azure Search also provides similar features, it may require additional customization and configuration.
- Developer Experience: Algolia is designed to provide a developer-friendly experience with comprehensive documentation, SDKs, and community support. It aims to simplify the integration process and make search implementation easier for developers. Azure Search, although developer-friendly, may have a slight learning curve and may require more effort for initial setup and customization.
- Ecosystem Integration: Azure Search is part of the wider Microsoft Azure ecosystem and integrates well with other Azure services, such as Azure Functions, Azure Logic Apps, and Azure Cognitive Services. This integration allows developers to leverage the power of the entire Azure platform for building comprehensive solutions. Algolia, while it provides its own integrations and libraries, may not have the same depth of ecosystem integration as Azure Search.
- Data Source Connectivity: Azure Search offers a wide range of data source connectors, including Azure Blob Storage, Azure Cosmos DB, SQL Server, and more, making it easy to ingest data from various sources. Algolia primarily focuses on indexing JSON data through its APIs, which may require additional data transformation and processing tasks for different data sources.
In summary, Algolia and Azure Search differ in their pricing model, scalability, advanced search features, developer experience, ecosystem integration, and data source connectivity. The choice between the two largely depends on the specific requirements of the project and the preferences of the development team.
I want to design a search engine which can search with PAYMENT-ID, ORDER-ID, CUSTOMER-NAME, CUSTOMER-PHONE, STORE-NAME, STORE-NUMBER, RETAILER-NAME, RETAILER-NUMBER, RETAILER-ID, RETAILER-MARKETPLACE-ID.
All these details are stored in different tables like ORDERS, PAYMENTS, RETAILERS, STORES, CUSTOMERS, and INVOICES with relations. Right now we have only 10MBs of data with 20K records. So I need a scalable solution that can handle the search from all the tables mentioned and how can I make a dataset with so many tables with relations for search.
What e-commerce platform or framework are you using?
A lot of this depends on what your infrastructure already supports. Either of the options are a great choice so it comes down to what will be easiest to integrate and which search service is most affordable.
Elastic search is open source but you will need to configure and maintain it on your server. It may be more difficult to set up depending on the platform your app is built on.
Algolia has great documentation and is normally pretty easy to integrate but it can be pretty expensive.
I've never used Typsense but it seems like it would be a great option as well.
Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?
(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.
Thank you!
Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.
To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.
Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.
For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.
Hope this helps.
We originally had used Algolia for our search features. It's a great service, however the cost was getting to be unmanageable for us. Algolia's pricing model is based around the number of search requests and the number of records. So if you produce a large number of small records the price can quickly get out of hand even if your actual dataset doesn't take up that much space on disk. Spikes in internet traffic can also lead to unexpected increases in billing (even if the traffic comes from bots)
After migrating to Typesense Cloud we have been able to save A LOT of money without losing out on any of the performance we got from Algolia. I do not exaggerate when I say that our overhead for search is less than 25% of what it used to be. Typesense also has the following advantages:
Their cloud offering lets you configure your Typesense nodes and specify how many you want to spin up. This allows you to manage costs in a manner that is way more predictable. (You basically pay for servers/nodes instead of records and requests)
It's completely open source. We can spin up a cluster on our own servers or run it locally.
The new pricing model Algolia introduced really sealed the deal for us on this one - much closer to pay-as-you-go. And didn't want to spend time learning more about hosting/optimizing Elasticsearch when that isn't our core business problem - would much rather pay others to solve that problem for us.
Pros of Algolia
- Ultra fast126
- Super easy to implement95
- Modern search engine73
- Excellent support71
- Easy setup, fast and relevant70
- Typos handling46
- Search analytics40
- Distributed Search Network31
- Designed to search records, not pages31
- Multiple datacenters30
- Smart Highlighting10
- Search as you type9
- Multi-attributes8
- Instantsearch.js8
- Super fast, easy to set up6
- Amazing uptime5
- Database search5
- Highly customizable4
- Great documentation4
- Github-awesome-autocomple4
- Realtime4
- Powerful Search3
- Places.js3
- Beautiful UI3
- Ok to use2
- Integrates with just about everything2
- Awesome aanltiycs and typos hnadling2
- Developer-friendly frontend libraries1
- Smooth platform1
- Fast response time1
- Github integration1
- Nooo0
- Fuck0
- Giitera0
- Is it fool0
Pros of Azure Search
- Easy to set up4
- Auto-Scaling3
- Managed3
- Easy Setup2
- More languages2
- Lucene based search criteria2
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Cons of Algolia
- Expensive11